Bibliographic record
Abstract
This study examines the processes and mechanisms by which a logic evolves over time. Employing a longitudinal cross-level research design, we trace the evolution of an elite group of higher education establishments, the French Grandes Écoles of Commerce (FGEC), from the late 1800s to the present. We draw on archival and interview data to show how changes in the broader socio-political environment shifted the attention of the FGEC to different referent audiences and to alternative normative orders, which prescribed different sets of practices and criteria for legitimacy. These changes prompted brief phases of institutional flux, wherein the FGEC were confronted with a heightened level of institutional complexity. This complexity increased organizational discretion and provided the resources needed to mobilize change. Comparing two important periods of change, we identify conditions that are likely to trigger particular responses – and how these responses act to recursively shape how a logic evolves. Building on these findings, we develop an analytical framework outlining six ideal-type forms of intra-logic persistence and change. These ideal types can be organized along three broad dimensions: cultural-symbolic, structural- relational, and material. We conclude by discussing the implications of our framework and how it can be applied to other research settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".